Compiler Design for Distributed Quantum Computing
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Abstract (arXiv)
In distributed quantum computing architectures, with the network and communications functionalities provided by the Quantum Internet, remote quantum processing units (QPUs) can communicate and cooperate for executing computational tasks that single NISQ devices cannot handle by themselves. To this aim, distributed quantum computing requires a new generation of quantum compilers, for mapping any quantum algorithm to any distributed quantum computing architecture. With this perspective, in this paper, we first discuss the main challenges arising with compiler design for distributed quantum computing. Then, we analytically derive an upper bound of the overhead induced by quantum compilation for distributed quantum computing. The derived bound accounts for the overhead induced by the underlying computing architecture as well as the additional overhead induced by the sub-optimal quantum compiler -- expressly designed through the paper to achieve three key features, namely, general-purpose, efficient and effective. Finally, we validate the analytical results and we confirm the validity of the compiler design through an extensive performance analysis.
Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). First posted on arXiv 17 Dec 2020.
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Citing papers per year: 2024: 1; 2025: 4; 2026: 1.
6 papers.
- 2025When Federated Learning Meets Quantum Computing: Survey and Research Opportunitiescited by 5
- 2025Adversarial Robustness of Partitioned Quantum Classifierscited by 2
- 2025Adversarial Robustness in Distributed Quantum Machine Learningcited by 2
- 2025RELiQ: Scalable Entanglement Routing via Reinforcement Learning in Quantum Networkscited by 2
- 2024Profiling quantum circuits for their efficient execution on single- and multi-core architecturescited by 0
- 2026Distributed Quantum Learning over Near-term Devices: Convergence Analysis and Security Designcited by 0
- 2026Distributed Quantum Learning over Near-term Devices: Convergence Analysis and Security Design21 May 2026
- 2025RELiQ: Scalable Entanglement Routing via Reinforcement Learning in Quantum Networks27 Nov 2025
- 2025Adversarial Robustness in Distributed Quantum Machine Learning16 Aug 2025
- 2025When Federated Learning Meets Quantum Computing: Survey and Research Opportunities9 Apr 2025
- 2025Adversarial Robustness of Partitioned Quantum Classifiers28 Jan 2025
- 2024Profiling quantum circuits for their efficient execution on single- and multi-core architectures17 Jul 2024